Study design considerations


  • Your capstone paper should mirror the scientific process, so you need to think critically about the sources and information you use


  • When reading primary research papers, it is important to understand and evaluate the way a study was performed:
    • Is the question appropriate? (i.e. testable/answerable)
    • Is the experimental approach logical?
    • What types of methods/instruments/sites were involved?
    • How were the results assessed? What was measured? Does it make sense to measure it? Is there a better way to measure it?


  • The same is true if you are designing/performing research

Evaluating experimental results

Evaluating experimental results

Evaluating experimental results
















How different is “different enough” between two groups to declare an effect?


How many times do you need to sample something to be representative?

Statistics to the rescue!

Expectations for a scientific paper



  • Goal is to use collected observations to make the strongest possible conclusion about whether to accept a hypothesis


  • So…
    • Experimental data should be quantified and analyzed appropriately
    • Large groups of data should be summarized for the reader


  • Expectations for the use of statistics is higher in some fields of biology than in others
    • Generally, ecological studies make greater use of statistics

Descriptive Statistics

  • Procedures that organize, summarize, or present data in an informative way

    • describe aspects of center and spread in data

  • Central tendency = a single value that describes a dataset by identifying the central position

  • The MEAN is the most common statistic used for this purpose

    • Example: the average score on an exam in a class

Aspects of Spread: Variance

  • The spread of distribution, or VARIANCE, around the mean is also important information














  • These three data sets have the same mean but different VARIANCES


  • Higher variance indicates that the values in the data set are more spread out

Standard deviation is a common measure of variance


  • Standard deviation = a measure of how far each value in the data set is from the mean


  • A higher standard deviation for your data set means you have a greater spread of values

Inferential statistics


  • Results often represent a portion of the overall population, but can be used to estimate information about the entire population with some amount of uncertainty













  • The larger your sample size, the more representative it is of the entire population
    • increasing the number of measurements increases the power of a statistical analysis

Hypothesis testing is widely used in science

  • Hypothesis testing:
    • Determine if there is a correlation between two variables
      • If X increases, does Y also increase? (positive)
      • If X decreases, does Y also decrease? (negative)











  • Determine if there are differences between two data sets
    • one group receives drug, the other does not
    • number of species in one location versus another location
    • amount of cell death after treatment with a drug

The null hypothesis proposes no relationship

  • A null hypothesis is a type of hypothesis used in statistics that proposes that there no relationship or difference between two parameters (e.g. two experimental groups, an independent variable and a dependent variable)—in other words, the results are due to chance alone
    • For any experiment, there is a null hypothesis


  • We aren’t proving things in science! The rejection of hypotheses is how we progress in science


  • Statistical tests allow us to use observed data to reject a null hypothesis and accept the alternative: that there is a relationship or difference

Experimental design (according to a statistician)




1. Set up the null and alternative hypotheses


2. Determine what type and how much data you will need to collect in your study


3. Choose a statistical analysis that will allow you to accept or reject your null hypothesis at a desired confidence level


4. Perform your experiment, analyze data, and draw conclusions

Brainstorm: What are some limits to experimental design?

How statistical significance is reported

  • When you perform a statistical test, a p-value helps you determine the significance of your results in relation to the null hypothesis.


  • Scientific papers will always report p-values if results have been statistically analyzed

Setting the bar for statistical significance


  • p-values range between 0 and 1 and indicate the probability that the null hypothesis is true
    • how likely are results are due to chance alone


  • The smaller the p-value, the stronger the evidence that you should reject the null hypothesis
    • i.e. the observed difference is NOT due to chance


  • A standard value used for significance in science is p = .05. This means you can conclude that 95% of the time, an observed effect is NOT due to chance

Important considerations for statistics


Both descriptive and inferential statistics whittle down large data sets down to a few conclusions


  • Statistical conclusions are never 100% certain
    • always a small chance that the null hypothesis provides a poor explanation of the data even when it is true.
    • always a small chance that the null hypothesis provides an explanation of the data even when it is false


  • A significant test can be misleading if the sample size is so small that an important effect goes undetected

How to talk about statistics in a paper


  • Unless you have a compelling reason, you do not need to cite specific p values of other researchers in your paper


  • Be decisive in your language—if a peer-reviewed primary paper reaches a conclusion, don’t hedge, but state it as fact
    • the reader has the citation if they want to evaluate that author’s work


The basis for downregulation may be related to CO2-induced excess photosynthate accumulation in leaves, if sinks for the additional carbon are not available (Stitt 1991, Mooreet al. 1999, Haouari et al. 2013, Campany et al. 2017).


It was reported that gm increased with increasing light intensity in chickpea and several Eucalyptus species (Campany et al., 2016; Xiong et al., 2018; Shrestha et al., 2019).